Python dim_ordering_reshape Exemples

Exemple #1

def model_generator():
    nch = 256
    g_input = Input(shape=[100])
    H = Dense(nch * 14 * 14, init='glorot_normal')(g_input)
    H = BatchNormalization(mode=2)(H)
    H = Activation('relu')(H)
    H = dim_ordering_reshape(nch, 14)(H)
    H = UpSampling2D(size=(2, 2))(H)
    H = Convolution2D(int(nch / 2),
                      3,
                      3,
                      border_mode='same',
                      init='glorot_uniform')(H)
    H = BatchNormalization(mode=2, axis=1)(H)
    H = Activation('relu')(H)
    H = Convolution2D(int(nch / 4),
                      3,
                      3,
                      border_mode='same',
                      init='glorot_uniform')(H)
    H = BatchNormalization(mode=2, axis=1)(H)
    H = Activation('relu')(H)
    H = Convolution2D(1, 1, 1, border_mode='same', init='glorot_uniform')(H)
    g_V = Activation('sigmoid')(H)
    return Model(g_input, g_V)

Exemple #2

Fichier : main.py Projet : okabe-yuya/language-study

def model_generator():
    ch_num = 256
    g_input = Input(shape=[100])
    H = Dense(ch_num * 14 * 14)(g_input)
    H = BatchNormalization()(H)
    H = Activation('relu')(H)
    H = dim_ordering_reshape(ch_num, 14)(H)
    H = UpSampling2D(size=(2, 2))(H)
    H = Conv2D(int(ch_num / 2), (3, 3), padding='same')(H)
    H = BatchNormalization()(H)
    H = Activation('relu')(H)
    H = Conv2D(int(ch_num / 4), (3, 3), padding='same')(H)
    H = BatchNormalization()(H)
    H = Activation('relu')(H)
    H = Conv2D(1, (1, 1), padding='same')(H)
    g_V = Activation('sigmoid')(H)
    return Model(g_input, g_V)

Exemple #3

Fichier : load.py Projet : pimaproject/Code-projet

def model_generator():
    nch = 128
    g_input = Input(shape=[400])
    H = Dense(nch * 46 * 46)(g_input)
    H = BatchNormalization(mode=2)(H)
    H = Activation('relu')(H)
    H = dim_ordering_reshape(nch, 46)(H)
    H = UpSampling2D(size=(2, 2))(H)
    H = Convolution2D(int(nch / 2), 3, 3, border_mode='same')(H)
    H = BatchNormalization(mode=2, axis=1)(H)
    H = Activation('relu')(H)
    H = Convolution2D(int(nch / 4), 3, 3, border_mode='same')(H)
    H = BatchNormalization(mode=2, axis=1)(H)
    H = Activation('relu')(H)
    H = Convolution2D(1, 1, 1, border_mode='same')(H)
    g_V = Activation('sigmoid')(H)
    return Model(g_input, g_V)

Exemple #4

Fichier : conv_GAN.py Projet : Yamagn/Keras-Learn

def model_generator():
    nch = 256
    g_input = Input(shape=[100])
    H = Dense(nch * 14 * 14)(g_input)
    H = BatchNormalization()(H)
    H = Activation("relu")(H)
    H = dim_ordering_reshape(nch, 14)(H)
    H = UpSampling2D(size=(2, 2))(H)
    H = Conv2D(int(nch / 2), (3, 3), padding="same")(H)
    H = BatchNormalization()(H)
    H = Activation("relu")(H)
    H = Conv2D(int(nch / 4), (3, 3), padding="same")(H)
    H = BatchNormalization()(H)
    H = Activation("relu")(H)
    H = Conv2D(1, (1, 1), padding="same")(H)
    g_V = Activation("sigmoid")(H)
    return Model(g_input, g_V)

Exemple #5

Fichier : example_gan_convolutional.py Projet : CeasarSS/books

def model_generator():
    nch = 256
    g_input = Input(shape=[100])
    H = Dense(nch * 14 * 14)(g_input)
    H = BatchNormalization(mode=2)(H)
    H = Activation('relu')(H)
    H = dim_ordering_reshape(nch, 14)(H)
    H = UpSampling2D(size=(2, 2))(H)
    H = Convolution2D(int(nch / 2), 3, 3, border_mode='same')(H)
    H = BatchNormalization(mode=2, axis=1)(H)
    H = Activation('relu')(H)
    H = Convolution2D(int(nch / 4), 3, 3, border_mode='same')(H)
    H = BatchNormalization(mode=2, axis=1)(H)
    H = Activation('relu')(H)
    H = Convolution2D(1, 1, 1, border_mode='same')(H)
    g_V = Activation('sigmoid')(H)
    return Model(g_input, g_V)

Exemple #6

def generator():
    c = 256
    g_input = Input(shape=[100])
    H = Dense(c * 14 * 14, kernel_initializer='glorot_normal')(g_input)
    H = BatchNormalization()(H)
    H = Activation('relu')(H)
    H = dim_ordering_reshape(c, 14)(H)
    H = UpSampling2D(size=(2, 2))(H)
    H = Convolution2D(int(c / 2),
                      kernel_size=3,
                      strides=3,
                      padding='same',
                      init='glorot_uniform')(H)
    H = BatchNormalization(axis=1)(H)
    H = Activation('relu')(H)
    H = Convolution2D(1,
                      kernel_size=1,
                      strides=1,
                      padding='same',
                      init='glorot_uniform')(H)
    g_V = Activation('sigmoid')(H)
    return Model(g_input, g_V)

Exemple #7

Fichier : Day4_Forgery.py Projet : maa105/ML-Day4

def model_generator():
    nch = 256
    g_input = Input(shape=[100])

    H = Dense(256 * 14 * 14, init='glorot_normal')(g_input)
    H = BatchNormalization()(H)
    H = Activation('relu')(H)

    H = dim_ordering_reshape(256, 14)
    H = UpSampling2D(size=(2, 2))(H)

    H = Conv2D(128, 3, 3, border_mode='same', init='glorot_normal')(H)
    H = BatchNormalization(axis=1)(H)
    H = Activation('relu')(H)

    H = Conv2D(64, 3, 3, border_mode='same', init='glorot_normal')(H)
    H = BatchNormalization(axis=1)(H)
    H = Activation('relu')(H)

    H = Conv2D(1, 1, 1, border_mode='same', init='glorot_normal')(H)
    g_V = Activation('sigmoid')(H)

    return Model(g_input, g_V)